Android robots shown at the Osaka Expo in a pavilion produced by University of Osaka professor Hiroshi Ishiguro will be relocated to Kyoto Prefecture. OSAKA - Seven android robots shown at the 2025 World Exposition in Osaka in a pavilion produced by University of Osaka professor Hiroshi Ishiguro will be relocated to Kyoto Prefecture after the end of the event on Monday. In addition, the Dutch pavilion will be moved to Awaji Island, Hyogo Prefecture. People involved in the use of expo assets after the event hope that they will be loved as tourist attractions in their new places. The prefectural government of Kyoto was chosen as the new owner of the androids in an open tender held by the expo organizer, the Japan Association for the 2025 World Exposition, in September. The robots will be shown to the public at a research facility in the Keihanna Science City research district straddling the Kyoto municipalities of Seika and Kizugawa.

Every video game is a miracle. Long hours, extraordinary technical and artistic requirements and cross-disciplinary collaboration: the very act of making games is difficult, and leaves room for catastrophic errors. It's a wonder any of them make it to release at all. Fulcrum Defender, the new Playdate exclusive from Jay Ma, the co-founder of indie darling Subset Games, is one such miraculous game. Ma began work on Fulcrum Defender following a life-changing Covid infection that has greatly diminished her quality of life and ability to do the thing she loves.

Nintendo was founded in 1889 in Kyoto, 100 years before the release of the Game Boy. Long before it was a video game company, it made toys and hanafuda cards adorned with scenes from nature, used to play several different games popular in Japan. By 1969, Nintendo had expanded its business to include western-style playing cards, and the company built a plant to manufacture them in southern Kyoto. Until 2016, the Uji Ogura Plant was a card factory and as a repairs centre for the company's consoles. It has been turned into a Nintendo Museum, opening on 2 October, where the gaming giant's entire history will be on display.

Generative AI, especially via Large Language Models (LLMs), has transformed content creation across text, images, and music, showcasing capabilities in following instructions through prompting, largely facilitated by instruction tuning. Instruction tuning is a supervised fine-tuning method where LLMs are trained on datasets formatted with specific tasks and corresponding instructions. This method systematically enhances the model's ability to comprehend and execute the provided directives. Despite these advancements, LLMs still face challenges in consistently interpreting complex, multi-step instructions and generalizing them to novel tasks, which are essential for broader applicability in real-world scenarios. This article explores why neurosymbolic AI offers a better path to enhance the instructability of LLMs. We explore the use a symbolic task planner to decompose high-level instructions into structured tasks, a neural semantic parser to ground these tasks into executable actions, and a neuro-symbolic executor to implement these actions while dynamically maintaining an explicit representation of state. We also seek to show that neurosymbolic approach enhances the reliability and context-awareness of task execution, enabling LLMs to dynamically interpret and respond to a wider range of instructional contexts with greater precision and flexibility.

Large-scale administrative data is a common input in early warning systems for college dropout in higher education. Still, the terminology and methodology vary significantly across existing studies, and the implications of different modeling decisions are not fully understood. This study provides a systematic evaluation of contributing factors and predictive performance of machine learning models over time and across different student groups. Drawing on twelve years of administrative data at a large public university in the US, we find that dropout prediction at the end of the second year has a 20% higher AUC than at the time of enrollment in a Random Forest model. Also, most predictive factors at the time of enrollment, including demographics and high school performance, are quickly superseded in predictive importance by college performance and in later stages by enrollment behavior. Regarding variability across student groups, college GPA has more predictive value for students from traditionally disadvantaged backgrounds than their peers. These results can help researchers and administrators understand the comparative value of different data sources when building early warning systems and optimizing decisions under specific policy goals.

Effective collaboration requires groups to strategically regulate themselves to overcome challenges. Research has shown that groups may fail to regulate due to differences in members' perceptions of challenges which may benefit from external support. In this study, we investigated the potential of leveraging three distinct natural language processing models: an expert knowledge rule-based model, a supervised machine learning (ML) model and a Large Language model (LLM), in challenge detection and challenge dimension identification (cognitive, metacognitive, emotional and technical/other challenges) from student discourse, was investigated. The results show that the supervised ML and the LLM approaches performed considerably well in both tasks, in contrast to the rule-based approach, whose efficacy heavily relies on the engineered features by experts. The paper provides an extensive discussion of the three approaches' performance for automated detection and support of students' challenge moments in collaborative learning activities. It argues that, although LLMs provide many advantages, they are unlikely to be the panacea to issues of the detection and feedback provision of socially shared regulation of learning due to their lack of reliability, as well as issues of validity evaluation, privacy and confabulation. We conclude the paper with a discussion on additional considerations, including model transparency to explore feasible and meaningful analytical feedback for students and educators using LLMs.

Numerous studies demonstrate the importance of self-regulation during learning by problem-solving. Recent work in learning analytics has largely examined students' use of SRL concerning overall learning gains. Limited research has related SRL to in-the-moment performance differences among learners. The present study investigates SRL behaviors in relationship to learners' moment-by-moment performance while working with intelligent tutoring systems for stoichiometry chemistry. We demonstrate the feasibility of labeling SRL behaviors based on AI-generated think-aloud transcripts, identifying the presence or absence of four SRL categories (processing information, planning, enacting, and realizing errors) in each utterance. Using the SRL codes, we conducted regression analyses to examine how the use of SRL in terms of presence, frequency, cyclical characteristics, and recency relate to student performance on subsequent steps in multi-step problems. A model considering students' SRL cycle characteristics outperformed a model only using in-the-moment SRL assessment. In line with theoretical predictions, students' actions during earlier, process-heavy stages of SRL cycles exhibited lower moment-by-moment correctness during problem-solving than later SRL cycle stages. We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.

A United Nations forum on public policy issues regarding the internet began in Kyoto on Sunday with focus on artificial intelligence and measures against disinformation. The results of the discussions at the Internet Governance Forum scheduled through Thursday will be utilized for the Hiroshima AI Process, in which the Group of Seven industrialized nations will establish rules on AI-related topics. Digital Minister Taro Kono attended as a panelist for a discussion titled "Understanding'Data Free Flow with Trust (DFFT),'" where he emphasized the need for more nations to join the dialogue. About 6,000 people from government, business and educational facilities are expected to attend the over 300 scheduled talks about cybercrime and the information gap born from differences in internet availability, among other topics. One session will look at measures against fake AI-generated video and audio that may be disseminated on social media.

Colon and Lung cancer is one of the most perilous and dangerous ailments that individuals are enduring worldwide and has become a general medical problem. To lessen the risk of death, a legitimate and early finding is particularly required. In any case, it is a truly troublesome task that depends on the experience of histopathologists. If a histologist is under-prepared it may even hazard the life of a patient. As of late, deep learning has picked up energy, and it is being valued in the analysis of Medical Imaging. This paper intends to utilize and alter the current pre-trained CNN-based model to identify lung and colon cancer utilizing histopathological images with better augmentation techniques. In this paper, eight distinctive Pre-trained CNN models, VGG16, NASNetMobile, InceptionV3, InceptionResNetV2, ResNet50, Xception, MobileNet, and DenseNet169 are trained on LC25000 dataset. The model performances are assessed on precision, recall, f1score, accuracy, and auroc score. The results exhibit that all eight models accomplished noteworthy results ranging from 96% to 100% accuracy. Subsequently, GradCAM and SmoothGrad are also used to picture the attention images of Pre-trained CNN models classifying malignant and benign images.

In 1977, Shigeru Miyamoto joined Nintendo, a company then known for selling toys, playing cards, and trivial novelties. Miyamoto was twenty-four, fresh out of art school. His employer, inspired by the success of a California company named Atari, was hoping to expand into video games. Miyamoto began tinkering with a story about a carpenter, a damsel in distress, and a giant ape. Four years later, Miyamoto had turned the carpenter into a plumber; Mario, and the Super Mario Bros. franchise, had arrived.